| Name | pylops-mpi JSON |
| Version |
0.3.0
JSON |
| download |
| home_page | None |
| Summary | Python library implementing linear operators with MPI |
| upload_time | 2025-08-05 22:07:19 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | GNU LESSER GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
This version of the GNU Lesser General Public License incorporates
the terms and conditions of version 3 of the GNU General Public
License, supplemented by the additional permissions listed below.
0. Additional Definitions.
As used herein, "this License" refers to version 3 of the GNU Lesser
General Public License, and the "GNU GPL" refers to version 3 of the GNU
General Public License.
"The Library" refers to a covered work governed by this License,
other than an Application or a Combined Work as defined below.
An "Application" is any work that makes use of an interface provided
by the Library, but which is not otherwise based on the Library.
Defining a subclass of a class defined by the Library is deemed a mode
of using an interface provided by the Library.
A "Combined Work" is a work produced by combining or linking an
Application with the Library. The particular version of the Library
with which the Combined Work was made is also called the "Linked
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The "Minimal Corresponding Source" for a Combined Work means the
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based on the Application, and not on the Linked Version.
The "Corresponding Application Code" for a Combined Work means the
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|
| keywords |
algebra
inverse problems
large-scale optimization
distributed computing
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://badge.fury.io/py/pylops-mpi)
[](https://github.com/PyLops/pylops-mpi/actions/workflows/build.yml)
[](https://github.com/PyLops/pylops-mpi/actions/workflows/pages/pages-build-deployment)
[](https://pylops.slack.com)
[](https://doi.org/10.21105/joss.07512)
# Distributed linear operators and solvers
Pylops-mpi is a Python library built on top of [PyLops](https://pylops.readthedocs.io/en/stable/), designed to enable distributed and parallel processing of
large-scale linear algebra operations and computations.
## Installation
To install pylops-mpi, you need to have Message Passing Interface (MPI) and optionally Nvidia's Collective Communication Library (NCCL) installed on your system.
1. **Download and Install MPI**: Visit the official MPI website to download an appropriate MPI implementation for your system.
Follow the installation instructions provided by the MPI vendor.
- [Open MPI](https://www.open-mpi.org/software/ompi/v1.10/)
- [MPICH](https://www.mpich.org/downloads/)
- [Intel MPI](https://www.intel.com/content/www/us/en/developer/tools/oneapi/mpi-library.html#gs.10j8fx)
2. **Verify MPI Installation**: After installing MPI, verify its installation by opening a terminal or command prompt
and running the following command:
```
mpiexec --version
```
3. **Install pylops-mpi**: Once MPI is installed and verified, you can proceed to install `pylops-mpi` via `pip`:
```
pip install pylops-mpi
```
4. (Optional) To enable the NCCL backend for multi-GPU systems, install `cupy` and `nccl` via `pip`:
```
pip install cupy-cudaXx nvidia-nccl-cuX
```
with `X=11,12`.
Alternatively, if the Conda package manager is used to setup the Python environment, steps 1 and 2 can be skipped and `mpi4py` can be installed directly alongside the MPI distribution of choice:
```
conda install -c conda-forge mpi4py X
```
with `X=mpich, openmpi, impi_rt, msmpi`. Similarly step 4 can be accomplished using:
```
conda install -c conda-forge cupy nccl
```
See the docs ([Installation](https://pylops.github.io/pylops-mpi/installation.html)) for more information.
## Run Pylops-MPI
Once you have installed the prerequisites and pylops-mpi, you can run pylops-mpi using the `mpiexec` command.
Here is an example on how to run a python script called `<script_name>.py`:
```
mpiexec -n <NUM_PROCESSES> python <script_name>.py
```
## Example: A distributed finite-difference operator
The following example is a modified version of
[PyLops' README](https://github.com/PyLops/pylops/blob/dev/README.md)_ starting
example that can handle a 2D-array distributed across ranks over the first dimension
via the `DistributedArray` object:
```python
import numpy as np
from pylops_mpi import DistributedArray, Partition
# Initialize DistributedArray with partition set to Scatter
nx, ny = 11, 21
x = np.zeros((nx, ny), dtype=np.float64)
x[nx // 2, ny // 2] = 1.0
x_dist = pylops_mpi.DistributedArray.to_dist(
x=x.flatten(),
partition=Partition.SCATTER)
# Distributed first-derivative
D_op = pylops_mpi.MPIFirstDerivative((nx, ny), dtype=np.float64)
# y = Dx
y_dist = D_op @ x_dist
# xadj = D^H y
xadj_dist = D_op.H @ y_dist
# xinv = D^-1 y
x0_dist = pylops_mpi.DistributedArray(D_op.shape[1], dtype=np.float64)
x0_dist[:] = 0
xinv_dist = pylops_mpi.cgls(D_op, y_dist, x0=x0_dist, niter=10)[0]
```
Note that the `DistributedArray` class provides the `to_dist` class method that accepts a NumPy array as input and converts it into an instance of the `DistributedArray` class. This method is used to transform a regular NumPy array into a DistributedArray that is distributed and processed across multiple nodes or processes.
Moreover, the `DistributedArray` class provides also fundamental mathematical operations, such as element-wise addition, subtraction, multiplication, dot product, and an equivalent of the [`np.linalg.norm`](https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html) function that operate in a distributed fashion,
thus utilizing the efficiency of the MPI/NCC; protocols. This enables efficient computation and processing of large-scale distributed arrays.
## Running Tests
The MPI test scripts are located in the `tests` folder.
Use the following command to run the tests:
```
mpiexec -n <NUM_PROCESSES> pytest tests/ --with-mpi
```
where the `--with-mpi` option tells pytest to enable the `pytest-mpi` plugin, allowing the tests to utilize the MPI functionality.
Similarly, to run the NCCL test scripts in the `tests_nccl` folder,
use the following command to run the tests:
```
mpiexec -n <NUM_PROCESSES> pytest tests_nccl/ --with-mpi
```
## Documentation
The official documentation of Pylops-MPI is available [here](https://pylops.github.io/pylops-mpi/).
Visit the official docs to learn more about pylops-mpi.
## Contributors
* Rohan Babbar, rohanbabbar04
* Yuxi Hong, hongyx11
* Matteo Ravasi, mrava87
* Tharit Tangkijwanichakul, tharittk
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"description": "\n\n[](https://badge.fury.io/py/pylops-mpi)\n[](https://github.com/PyLops/pylops-mpi/actions/workflows/build.yml)\n[](https://github.com/PyLops/pylops-mpi/actions/workflows/pages/pages-build-deployment)\n\n[](https://pylops.slack.com)\n[](https://doi.org/10.21105/joss.07512)\n\n# Distributed linear operators and solvers\nPylops-mpi is a Python library built on top of [PyLops](https://pylops.readthedocs.io/en/stable/), designed to enable distributed and parallel processing of \nlarge-scale linear algebra operations and computations. \n\n## Installation\nTo install pylops-mpi, you need to have Message Passing Interface (MPI) and optionally Nvidia's Collective Communication Library (NCCL) installed on your system.\n\n1. **Download and Install MPI**: Visit the official MPI website to download an appropriate MPI implementation for your system. \nFollow the installation instructions provided by the MPI vendor.\n - [Open MPI](https://www.open-mpi.org/software/ompi/v1.10/)\n - [MPICH](https://www.mpich.org/downloads/)\n - [Intel MPI](https://www.intel.com/content/www/us/en/developer/tools/oneapi/mpi-library.html#gs.10j8fx)\n\n2. **Verify MPI Installation**: After installing MPI, verify its installation by opening a terminal or command prompt \nand running the following command:\n ```\n mpiexec --version\n ```\n\n3. **Install pylops-mpi**: Once MPI is installed and verified, you can proceed to install `pylops-mpi` via `pip`:\n ```\n pip install pylops-mpi\n ```\n\n4. (Optional) To enable the NCCL backend for multi-GPU systems, install `cupy` and `nccl` via `pip`:\n ```\n pip install cupy-cudaXx nvidia-nccl-cuX\n ```\n \n with `X=11,12`.\n\nAlternatively, if the Conda package manager is used to setup the Python environment, steps 1 and 2 can be skipped and `mpi4py` can be installed directly alongside the MPI distribution of choice:\n\n```\nconda install -c conda-forge mpi4py X\n```\n\nwith `X=mpich, openmpi, impi_rt, msmpi`. Similarly step 4 can be accomplished using:\n\n```\nconda install -c conda-forge cupy nccl \n```\n\nSee the docs ([Installation](https://pylops.github.io/pylops-mpi/installation.html)) for more information.\n\n## Run Pylops-MPI\nOnce you have installed the prerequisites and pylops-mpi, you can run pylops-mpi using the `mpiexec` command. \n\nHere is an example on how to run a python script called `<script_name>.py`:\n```\nmpiexec -n <NUM_PROCESSES> python <script_name>.py\n```\n\n## Example: A distributed finite-difference operator\nThe following example is a modified version of \n[PyLops' README](https://github.com/PyLops/pylops/blob/dev/README.md)_ starting \nexample that can handle a 2D-array distributed across ranks over the first dimension \nvia the `DistributedArray` object:\n\n```python\nimport numpy as np\nfrom pylops_mpi import DistributedArray, Partition\n\n# Initialize DistributedArray with partition set to Scatter\nnx, ny = 11, 21\nx = np.zeros((nx, ny), dtype=np.float64)\nx[nx // 2, ny // 2] = 1.0\n\nx_dist = pylops_mpi.DistributedArray.to_dist(\n x=x.flatten(), \n partition=Partition.SCATTER)\n\n# Distributed first-derivative\nD_op = pylops_mpi.MPIFirstDerivative((nx, ny), dtype=np.float64)\n\n# y = Dx\ny_dist = D_op @ x_dist\n\n# xadj = D^H y\nxadj_dist = D_op.H @ y_dist\n\n# xinv = D^-1 y\nx0_dist = pylops_mpi.DistributedArray(D_op.shape[1], dtype=np.float64)\nx0_dist[:] = 0\nxinv_dist = pylops_mpi.cgls(D_op, y_dist, x0=x0_dist, niter=10)[0]\n```\n\nNote that the `DistributedArray` class provides the `to_dist` class method that accepts a NumPy array as input and converts it into an instance of the `DistributedArray` class. This method is used to transform a regular NumPy array into a DistributedArray that is distributed and processed across multiple nodes or processes.\n\nMoreover, the `DistributedArray` class provides also fundamental mathematical operations, such as element-wise addition, subtraction, multiplication, dot product, and an equivalent of the [`np.linalg.norm`](https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html) function that operate in a distributed fashion, \nthus utilizing the efficiency of the MPI/NCC; protocols. This enables efficient computation and processing of large-scale distributed arrays.\n\n## Running Tests\nThe MPI test scripts are located in the `tests` folder.\nUse the following command to run the tests:\n```\nmpiexec -n <NUM_PROCESSES> pytest tests/ --with-mpi\n```\nwhere the `--with-mpi` option tells pytest to enable the `pytest-mpi` plugin, allowing the tests to utilize the MPI functionality.\n\nSimilarly, to run the NCCL test scripts in the `tests_nccl` folder, \nuse the following command to run the tests:\n```\nmpiexec -n <NUM_PROCESSES> pytest tests_nccl/ --with-mpi\n```\n\n## Documentation \nThe official documentation of Pylops-MPI is available [here](https://pylops.github.io/pylops-mpi/).\nVisit the official docs to learn more about pylops-mpi.\n\n## Contributors\n* Rohan Babbar, rohanbabbar04\n* Yuxi Hong, hongyx11\n* Matteo Ravasi, mrava87\n* Tharit Tangkijwanichakul, tharittk\n",
"bugtrack_url": null,
"license": "GNU LESSER GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n \n Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n \n \n This version of the GNU Lesser General Public License incorporates\n the terms and conditions of version 3 of the GNU General Public\n License, supplemented by the additional permissions listed below.\n \n 0. Additional Definitions.\n \n As used herein, \"this License\" refers to version 3 of the GNU Lesser\n General Public License, and the \"GNU GPL\" refers to version 3 of the GNU\n General Public License.\n \n \"The Library\" refers to a covered work governed by this License,\n other than an Application or a Combined Work as defined below.\n \n An \"Application\" is any work that makes use of an interface provided\n by the Library, but which is not otherwise based on the Library.\n Defining a subclass of a class defined by the Library is deemed a mode\n of using an interface provided by the Library.\n \n A \"Combined Work\" is a work produced by combining or linking an\n Application with the Library. The particular version of the Library\n with which the Combined Work was made is also called the \"Linked\n Version\".\n \n The \"Minimal Corresponding Source\" for a Combined Work means the\n Corresponding Source for the Combined Work, excluding any source code\n for portions of the Combined Work that, considered in isolation, are\n based on the Application, and not on the Linked Version.\n \n The \"Corresponding Application Code\" for a Combined Work means the\n object code and/or source code for the Application, including any data\n and utility programs needed for reproducing the Combined Work from the\n Application, but excluding the System Libraries of the Combined Work.\n \n 1. Exception to Section 3 of the GNU GPL.\n \n You may convey a covered work under sections 3 and 4 of this License\n without being bound by section 3 of the GNU GPL.\n \n 2. Conveying Modified Versions.\n \n If you modify a copy of the Library, and, in your modifications, a\n facility refers to a function or data to be supplied by an Application\n that uses the facility (other than as an argument passed when the\n facility is invoked), then you may convey a copy of the modified\n version:\n \n a) under this License, provided that you make a good faith effort to\n ensure that, in the event an Application does not supply the\n function or data, the facility still operates, and performs\n whatever part of its purpose remains meaningful, or\n \n b) under the GNU GPL, with none of the additional permissions of\n this License applicable to that copy.\n \n 3. Object Code Incorporating Material from Library Header Files.\n \n The object code form of an Application may incorporate material from\n a header file that is part of the Library. You may convey such object\n code under terms of your choice, provided that, if the incorporated\n material is not limited to numerical parameters, data structure\n layouts and accessors, or small macros, inline functions and templates\n (ten or fewer lines in length), you do both of the following:\n \n a) Give prominent notice with each copy of the object code that the\n Library is used in it and that the Library and its use are\n covered by this License.\n \n b) Accompany the object code with a copy of the GNU GPL and this license\n document.\n \n 4. Combined Works.\n \n You may convey a Combined Work under terms of your choice that,\n taken together, effectively do not restrict modification of the\n portions of the Library contained in the Combined Work and reverse\n engineering for debugging such modifications, if you also do each of\n the following:\n \n a) Give prominent notice with each copy of the Combined Work that\n the Library is used in it and that the Library and its use are\n covered by this License.\n \n b) Accompany the Combined Work with a copy of the GNU GPL and this license\n document.\n \n c) For a Combined Work that displays copyright notices during\n execution, include the copyright notice for the Library among\n these notices, as well as a reference directing the user to the\n copies of the GNU GPL and this license document.\n \n d) Do one of the following:\n \n 0) Convey the Minimal Corresponding Source under the terms of this\n License, and the Corresponding Application Code in a form\n suitable for, and under terms that permit, the user to\n recombine or relink the Application with a modified version of\n the Linked Version to produce a modified Combined Work, in the\n manner specified by section 6 of the GNU GPL for conveying\n Corresponding Source.\n \n 1) Use a suitable shared library mechanism for linking with the\n Library. A suitable mechanism is one that (a) uses at run time\n a copy of the Library already present on the user's computer\n system, and (b) will operate properly with a modified version\n of the Library that is interface-compatible with the Linked\n Version.\n \n e) Provide Installation Information, but only if you would otherwise\n be required to provide such information under section 6 of the\n GNU GPL, and only to the extent that such information is\n necessary to install and execute a modified version of the\n Combined Work produced by recombining or relinking the\n Application with a modified version of the Linked Version. (If\n you use option 4d0, the Installation Information must accompany\n the Minimal Corresponding Source and Corresponding Application\n Code. If you use option 4d1, you must provide the Installation\n Information in the manner specified by section 6 of the GNU GPL\n for conveying Corresponding Source.)\n \n 5. Combined Libraries.\n \n You may place library facilities that are a work based on the\n Library side by side in a single library together with other library\n facilities that are not Applications and are not covered by this\n License, and convey such a combined library under terms of your\n choice, if you do both of the following:\n \n a) Accompany the combined library with a copy of the same work based\n on the Library, uncombined with any other library facilities,\n conveyed under the terms of this License.\n \n b) Give prominent notice with the combined library that part of it\n is a work based on the Library, and explaining where to find the\n accompanying uncombined form of the same work.\n \n 6. Revised Versions of the GNU Lesser General Public License.\n \n The Free Software Foundation may publish revised and/or new versions\n of the GNU Lesser General Public License from time to time. Such new\n versions will be similar in spirit to the present version, but may\n differ in detail to address new problems or concerns.\n \n Each version is given a distinguishing version number. If the\n Library as you received it specifies that a certain numbered version\n of the GNU Lesser General Public License \"or any later version\"\n applies to it, you have the option of following the terms and\n conditions either of that published version or of any later version\n published by the Free Software Foundation. If the Library as you\n received it does not specify a version number of the GNU Lesser\n General Public License, you may choose any version of the GNU Lesser\n General Public License ever published by the Free Software Foundation.\n \n If the Library as you received it specifies that a proxy can decide\n whether future versions of the GNU Lesser General Public License shall\n apply, that proxy's public statement of acceptance of any version is\n permanent authorization for you to choose that version for the\n Library.\n ",
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